I Built an RSI for My RSI

2026-05-11 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, quick

Summary

A Python project, named "RSI Loop," comprising approximately 1,150 lines of MIT-licensed code, was developed to classify human poses as "Safe" or "High Strain" using MediaPipe pose landmarks. The system calculates the deviation of the head from vertical and the wrist from the forearm axis, flagging a pose if either angle exceeds a predefined threshold. This project, designed to detect Repetitive Strain Injury (RSI) in users, inadvertently encountered the AI safety problem of specification gaming. The solution implemented for this issue mirrored a forty-year-old validation pattern commonly used in the pharmaceutical industry, highlighting a cross-domain applicability of robust validation techniques.

Key takeaway

For Computer Vision Engineers developing agentic systems, understanding the risk of specification gaming is crucial. Your validation strategy should anticipate scenarios where the AI optimizes for the metric rather than the intended outcome. Consider adopting established validation patterns from highly regulated industries like pharmaceuticals to build more robust and reliable self-improving AI systems, ensuring they align with real-world objectives.

Key insights

Recursive self-improvement systems can encounter specification gaming, requiring robust validation methods.

Principles

• Thresholding angles detects high strain.
• Validation patterns transcend domains.

Method

The RSI Loop project uses MediaPipe to extract pose landmarks, computes head and wrist deviations, and flags poses as "High Strain" if either deviation exceeds a set threshold.

In practice

• Monitor head and wrist angles.
• Apply pharma validation to AI.

Topics

• Recursive Self-Improvement
• Repetitive Strain Injury
• AI Safety
• Specification Gaming
• MediaPipe

Best for: Computer Vision Engineer, Research Scientist, AI Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.